Cortisol as a Marker of Pain and Distress After Acute Musculoskeletal Trauma
Bibliographic record
Abstract
OBJECTIVES: The best available predictors of chronic pain development broadly encompass baseline metrics of cognition (ie, beliefs/expectations) about the trauma and resulting symptoms. In the context of musculoskeletal trauma, we have previously shown the Traumatic Injuries Distress Scale (TIDS) capable of risk-stratifying cohorts for chronic pain development. Here, we explore whether the physiological marker cortisol shows meaningful associations with cognitions predictive of pain outcomes. METHODS: Data for these cross-sectional analyses were drawn from an observational study that recruited 130 participants presenting to the hospital with pain related to a recent noncatastrophic Musculoskeletal trauma. Cortisol was measured from the participant's hair, saliva, and blood. In addition to pain and distress questionnaires, metadata such as age, sex, body mass index (BMI), adverse childhood events, pretrauma stress levels, and pre-existing physical/psychological comorbidities were collected. RESULTS: We found no significant associations between cortisol levels and pain or distress in isolation. When stratified by person-level variables, associations were revealed with distress (TIDS) in young age and low pretrauma stress subgroups (hair cortisol) and low BMI (blood cortisol). Through hierarchical regression analysis, we found the "cortisol X age" or "cortisol X stress" interaction terms significantly improved TIDS prediction beyond either variable in isolation (Age: ∆ R2 =15.1%; pretrauma stress: ∆ R2 =9.1%). DISCUSSION: Our findings suggest that while linear correlations between pain-related distress and cortisol may be overly simplistic, certain person-level variables such as age, pretrauma stress, and BMI are worthy of consideration for experimental design or confounder characterization in future studies of pain and distress following musculoskeletal injuries especially when "trait" (hair) cortisol is the predictor variable.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".